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The use of artificial intelligence in education (AIEd) has become increasingly significant globally. In China, there is a lack of research examining the behavioral intention toward AIEd among pre-service special education (SPED) teachers in terms of digital literacy and teacher self-efficacy. Building on the technology acceptance model, our study evaluated the aspects influencing pre-service special education teachers' intention toward AI in education. Data was gathered from 274 pre-service SPED teachers studying at a Chinese public normal university of special education and analyzed using structural equation modeling (SEM). The results show that digital literacy is associated with the perceived usefulness and ease of use of AIEd, which influences SPED teachers' intention to use AIEd. Additionally, digital literacy significantly impacts the self-efficacy of SPED teachers. Given these results, AI designers in special education should comprehend the effectiveness and usability of AIEd for fostering behavioral intention formation. Simultaneously, special educational programs that identify key content and activities for digital literacy training should be developed, and educators should attempt to execute the relevant pre-service training to enhance the intention of pre-service SPED teachers toward AIEd.

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This paper investigates the factors influencing the continuous use intention of AI-powered adaptive learning systems among rural middle school students in China. Employing a mixed-method approach, this study integrates Technology Acceptance Model 3 with empirical data collected from rural middle schools in western China. The main contributions of this study include identifying key determinants of usage intention, such as computer self-efficacy, perceived enjoyment, system quality, and the perception of feedback. The findings provide insights into enhancing rural education through AI and suggest strategies for developing more effective and engaging adaptive learning systems. This research not only fills a significant gap in the understanding of AI in education but also offers practical implications for educators and policymakers aiming to improve learning outcomes in rural settings.

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As artificial intelligence systems gain traction, their trustworthiness becomes paramount to harness their benefits and mitigate risks. This study underscores the pressing need for an expectation management framework to align stakeholder anticipations before any system-related activities, such as data collection, modeling, or implementation. To this end, we introduce a comprehensive framework tailored to capture end-user expectations specifically for trustworthy artificial intelligence systems. To ensure its relevance and robustness, we validated the framework via semi-structured interviews, encompassing questions rooted in the framework's constructs and principles. These interviews engaged fourteen diverse end users across the healthcare and education sectors, including physicians, teachers, and students. Through a meticulous qualitative analysis of the interview transcripts, we unearthed pivotal themes and discerned varying perspectives among the interviewee groups. Ultimately, our framework stands as a pivotal tool, paving the way for in-depth discussions about user expectations, illuminating the significance of various system attributes, and spotlighting potential challenges that might jeopardize the system's efficacy.

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This mixed-method study explores the acceptance of ChatGPT as a tool for Metacognitive Self-Regulated Learning (MSRL) among academics. Despite the growing attention towards ChatGPT as a metacognitive learning tool, there is a need for a comprehensive understanding of the factors influencing its acceptance in academic settings. Engaging 300 preservice teachers through a ChatGPT-based scenario learning activity and utilizing convenience sampling, this study administered a questionnaire based on the proposed Technology Acceptance Model at UTM University's School of Education. Structural equation modelling was applied to analyze participants' perspectives on ChatGPT, considering factors like MSRL's impact on usage intention. Post-reflection sessions, semi-structured interviews, and record analysis were conducted to gather results. Findings indicate a high acceptance of ChatGPT, significantly influenced by personal competency, social influence, perceived AI usefulness, enjoyment, trust, AI intelligence, positive attitude, and metacognitive self-regulated learning. Interviews and record analysis suggest that academics view ChatGPT positively as an educational tool, seeing it as a solution to challenges in teaching and learning processes. The study highlights ChatGPT's potential to enhance MSRL and holds implications for teacher education and AI integration in educational settings.

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The impact of artificial intelligence (AI) in education can be viewed as a multi-attribute group decision-making (MAGDM) problem, in which several stakeholders evaluate the advantages and disadvantages of AI applications in educational settings according to distinct preferences and criteria. A MAGDM framework can assist in providing transparent and logical recommendations for implementing AI in education by methodically analyzing the trade-offs and conflicts among many components, including ethical, social, pedagogical, and technical concerns. A novel development in fuzzy set theory is the 2-tuple linguistic q-rung orthopair fuzzy set (2TLq-ROFS), which is not only a generalized form but also can integrate decision-makers quantitative evaluation ideas and qualitative evaluation information. The 2TLq-ROF Schweizer-Sklar weighted power average operator (2TLq-ROFSSWPA) and the 2TLq-ROF Schweizer-Sklar weighted power geometric (2TLq-ROFSSWPG) operator are two of the aggregation operators we create in this article. We also investigate some of the unique instances and features of the proposed operators. Next, a new Entropy model is built based on 2TLq-ROFS, which may exploit the preferences of the decision-makers to obtain the ideal objective weights for attributes. Next, we extend the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique to the 2TLq-ROF version, which provides decision-makers with a greater space to represent their decisions, while also accounting for the uncertainty inherent in human cognition. Finally, a case study of how artificial intelligence has impacted education is given to show the applicability and value of the established methodology. A comparative study is carried out to examine the benefits and improvements of the developed approach.

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The Radiological Society of North America (RSNA) has held artificial intelligence competitions to tackle real-world medical imaging problems at least annually since 2017. This article examines the challenges and processes involved in organizing these competitions, with a specific emphasis on the creation and curation of high-quality datasets. The collection of diverse and representative medical imaging data involves dealing with issues of patient privacy and data security. Furthermore, ensuring quality and consistency in data, which includes expert labeling and accounting for various patient and imaging characteristics, necessitates substantial planning and resources. Overcoming these obstacles requires meticulous project management and adherence to strict timelines. The article also highlights the potential of crowdsourced annotation to progress medical imaging research. Through the RSNA competitions, an effective global engagement has been realized, resulting in innovative solutions to complex medical imaging problems, thus potentially transforming health care by enhancing diagnostic accuracy and patient outcomes. Keywords: Use of AI in Education, Artificial Intelligence © RSNA, 2024.

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Students' affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students' affective states are determinative of the learning quality. However, measuring various affective states and what influences them is exceedingly challenging for the lecturer without having real interaction with the students. Existing studies primarily use self-reported data to understand students' affective states, while this paper presents a novel learning analytics system called MOEMO (Motion and Emotion) that could measure online learners' affective states of engagement and concentration using emotion data. Therefore, the novelty of this research is to visualize online learners' affective states on lecturers' screens in real-time using an automated emotion detection process. In real-time and offline, the system extracts emotion data by analyzing facial features from the lecture videos captured by the typical built-in web camera of a laptop computer. The system determines online learners' five types of engagement ("strong engagement", "high engagement", "medium engagement", "low engagement", and "disengagement") and two types of concentration levels ("focused" and "distracted"). Furthermore, the dashboard is designed to provide insight into students' emotional states, the clusters of engaged and disengaged students', assistance with intervention, create an after-class summary report, and configure the automation parameters to adapt to the study environment.

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Implementation of artificial intelligence (AI) applications into clinical practice requires AI-savvy radiologists to ensure the safe, ethical, and effective use of these systems for patient care. Increasing demand for AI education reflects recognition of the translation of AI applications from research to clinical practice, with positive trainee attitudes regarding the influence of AI on radiology. However, barriers to AI education, such as limited access to resources, predispose to insufficient preparation for the effective use of AI in practice. In response, national organizations have sponsored formal and self-directed learning courses to provide introductory content on imaging informatics and AI. Foundational courses, such as the National Imaging Informatics Course - Radiology and the Radiological Society of North America Imaging AI Certificate, lay a framework for trainees to explore the creation, deployment, and critical evaluation of AI applications. This report includes additional resources for formal programming courses, video series from leading organizations, and blogs from AI and informatics communities. Furthermore, the scope of "AI and radiology education" includes AI-augmented radiology education, with emphasis on the potential for "precision education" that creates personalized experiences for trainees by accounting for varying learning styles and inconsistent, possibly deficient, clinical case volume. © RSNA, 2022 Keywords: Use of AI in Education, Impact of AI on Education, Artificial Intelligence, Medical Education, Imaging Informatics, Natural Language Processing, Precision Education.

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The 1° Encontro Latino-Americano de IA em Saúde (1st Latin American Meeting on AI in Health) was held during the 2022 Jornada Paulista de Radiologia, the annual radiology meeting in the state of São Paulo. The event was created to foster discussion among Latin American countries about the complexity, challenges, and opportunities in developing and using artificial intelligence (AI) in those countries. Technological improvements in AI have created high expectations in health care. AI is recognized increasingly as a game changer in clinical radiology. To counter the fear that AI would "take over" radiology, the program included activities to educate radiologists. The development of AI in Latin America is in its early days, and although there are some pioneer cases, many regions still lack world-class technological infrastructure and resources. Legislation, regulation, and public policies in data privacy and protection, digital health, and AI are recent advances in many countries. The meeting program was developed with a broad scope, with expertise from different countries, backgrounds, and specialties, with the objective of encompassing all levels of complexity (from basic concepts to advanced techniques), perspectives (clinical, technical, ethical, and business), and specialties (both informatics and data science experts and the usual radiology clinical groups). It was an opportunity to connect with peers from other countries and share lessons learned about AI in health care in different countries and contexts. Keywords: Informatics, Use of AI in Education, Impact of AI on Education, Social Implications © RSNA, 2022.

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Purpose: To evaluate code and data sharing practices in original artificial intelligence (AI) scientific manuscripts published in the Radiological Society of North America (RSNA) journals suite from 2017 through 2021. Materials and Methods: A retrospective meta-research study was conducted of articles published in the RSNA journals suite from January 1, 2017, through December 31, 2021. A total of 218 articles were included and evaluated for code sharing practices, reproducibility of shared code, and data sharing practices. Categorical comparisons were conducted using Fisher exact tests with respect to year and journal of publication, author affiliation(s), and type of algorithm used. Results: Of the 218 included articles, 73 (34%) shared code, with 24 (33% of code sharing articles and 11% of all articles) sharing reproducible code. Radiology and Radiology: Artificial Intelligence published the most code sharing articles (48 [66%] and 21 [29%], respectively). Twenty-nine articles (13%) shared data, and 12 of these articles (41% of data sharing articles) shared complete experimental data by using only public domain datasets. Four of the 218 articles (2%) shared both code and complete experimental data. Code sharing rates were statistically higher in 2020 and 2021 compared with earlier years (P < .01) and were higher in Radiology and Radiology: Artificial Intelligence compared with other journals (P < .01). Conclusion: Original AI scientific articles in the RSNA journals suite had low rates of code and data sharing, emphasizing the need for open-source code and data to achieve transparent and reproducible science.Keywords: Meta-Analysis, AI in Education, Machine LearningSupplemental material is available for this article.© RSNA, 2022.

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Although artificial intelligence (AI) has immense potential to shape the future of medicine, its place in undergraduate medical education currently is unclear. Numerous arguments exist both for and against including AI in the medical school curriculum. AI likely will affect all medical specialties, perhaps radiology more so than any other. The purpose of this article is to present a balanced perspective on whether AI should be included officially in the medical school curriculum. After presenting the balanced point-counterpoint arguments, the authors provide a compromise. Keywords: Artificial Intelligence, Medical Education, Medical School Curriculum, Medical Students, Radiology, Use of AI in Education © RSNA, 2022.

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Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work. Keywords: Use of AI in Education, Application Domain, Supervised Learning, Safety © RSNA, 2022.